Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations1587
Missing cells0
Missing cells (%)0.0%
Duplicate rows260
Duplicate rows (%)16.4%
Total size in memory161.2 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

Dataset has 260 (16.4%) duplicate rowsDuplicates
alcohol is highly overall correlated with chlorides and 1 other fieldsHigh correlation
chlorides is highly overall correlated with alcohol and 1 other fieldsHigh correlation
density is highly overall correlated with alcohol and 3 other fieldsHigh correlation
fixed acidity is highly overall correlated with pHHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
pH is highly overall correlated with fixed acidityHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
total sulfur dioxide is highly overall correlated with density and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-11-06 22:17:11.296660
Analysis finished2024-11-06 22:17:27.324671
Duration16.03 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5310649
Minimum3.8
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:27.540530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.4
Q16.1
median6.5
Q36.9
95-th percentile7.7
Maximum9.4
Range5.6
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.70669582
Coefficient of variation (CV)0.1082053
Kurtosis0.8444437
Mean6.5310649
Median Absolute Deviation (MAD)0.4
Skewness0.20778329
Sum10364.8
Variance0.49941898
MonotonicityNot monotonic
2024-11-06T19:17:27.674132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
6.6 118
 
7.4%
6.4 118
 
7.4%
6.8 103
 
6.5%
6 101
 
6.4%
6.7 91
 
5.7%
6.5 83
 
5.2%
6.2 79
 
5.0%
6.3 70
 
4.4%
6.1 70
 
4.4%
6.9 66
 
4.2%
Other values (38) 688
43.4%
ValueCountFrequency (%)
3.8 1
 
0.1%
3.9 1
 
0.1%
4.4 3
 
0.2%
4.7 5
 
0.3%
4.8 7
0.4%
4.9 4
 
0.3%
5 13
0.8%
5.1 10
0.6%
5.2 10
0.6%
5.3 13
0.8%
ValueCountFrequency (%)
9.4 1
 
0.1%
9 2
 
0.1%
8.9 3
0.2%
8.8 3
0.2%
8.7 2
 
0.1%
8.6 4
0.3%
8.5 2
 
0.1%
8.4 2
 
0.1%
8.3 6
0.4%
8.2 3
0.2%

volatile acidity
Real number (ℝ)

Distinct92
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28233459
Minimum0.085
Maximum1.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:27.809399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.16
Q10.22
median0.27
Q30.33
95-th percentile0.46
Maximum1.1
Range1.015
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.099181154
Coefficient of variation (CV)0.35128941
Kurtosis5.5554213
Mean0.28233459
Median Absolute Deviation (MAD)0.05
Skewness1.5889832
Sum448.065
Variance0.0098369012
MonotonicityNot monotonic
2024-11-06T19:17:27.928231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 96
 
6.0%
0.22 91
 
5.7%
0.24 89
 
5.6%
0.27 75
 
4.7%
0.26 70
 
4.4%
0.3 69
 
4.3%
0.32 64
 
4.0%
0.23 64
 
4.0%
0.2 63
 
4.0%
0.25 57
 
3.6%
Other values (82) 849
53.5%
ValueCountFrequency (%)
0.085 1
 
0.1%
0.09 1
 
0.1%
0.105 4
 
0.3%
0.11 5
 
0.3%
0.12 7
 
0.4%
0.13 8
 
0.5%
0.14 15
 
0.9%
0.145 2
 
0.1%
0.15 31
2.0%
0.16 46
2.9%
ValueCountFrequency (%)
1.1 1
0.1%
0.785 1
0.1%
0.76 1
0.1%
0.75 1
0.1%
0.73 1
0.1%
0.695 2
0.1%
0.69 2
0.1%
0.67 1
0.1%
0.66 1
0.1%
0.655 1
0.1%

citric acid
Real number (ℝ)

Distinct72
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30461248
Minimum0
Maximum1
Zeros8
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:28.078368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.25
median0.29
Q30.34
95-th percentile0.5
Maximum1
Range1
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.1049441
Coefficient of variation (CV)0.34451675
Kurtosis5.2689981
Mean0.30461248
Median Absolute Deviation (MAD)0.04
Skewness1.2672131
Sum483.42
Variance0.011013264
MonotonicityNot monotonic
2024-11-06T19:17:28.276664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 134
 
8.4%
0.3 113
 
7.1%
0.27 103
 
6.5%
0.32 94
 
5.9%
0.26 92
 
5.8%
0.29 88
 
5.5%
0.24 64
 
4.0%
0.33 62
 
3.9%
0.25 61
 
3.8%
0.31 55
 
3.5%
Other values (62) 721
45.4%
ValueCountFrequency (%)
0 8
0.5%
0.01 4
0.3%
0.02 3
 
0.2%
0.04 3
 
0.2%
0.05 1
 
0.1%
0.06 2
 
0.1%
0.09 7
0.4%
0.1 5
0.3%
0.11 1
 
0.1%
0.12 9
0.6%
ValueCountFrequency (%)
1 1
 
0.1%
0.91 2
0.1%
0.86 1
 
0.1%
0.82 1
 
0.1%
0.79 1
 
0.1%
0.78 1
 
0.1%
0.74 1
 
0.1%
0.73 1
 
0.1%
0.72 1
 
0.1%
0.71 3
0.2%

residual sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct214
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4099244
Minimum0.7
Maximum22.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:28.425714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.1
Q11.9
median5.3
Q39.8
95-th percentile15.5
Maximum22.6
Range21.9
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation4.8429969
Coefficient of variation (CV)0.75554666
Kurtosis-0.53079488
Mean6.4099244
Median Absolute Deviation (MAD)3.65
Skewness0.70085078
Sum10172.55
Variance23.454619
MonotonicityNot monotonic
2024-11-06T19:17:28.621369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 66
 
4.2%
1.1 52
 
3.3%
1.4 51
 
3.2%
1.3 50
 
3.2%
1.6 38
 
2.4%
1.5 35
 
2.2%
2 31
 
2.0%
1 29
 
1.8%
1.8 26
 
1.6%
1.9 22
 
1.4%
Other values (204) 1187
74.8%
ValueCountFrequency (%)
0.7 2
 
0.1%
0.8 5
 
0.3%
0.9 14
 
0.9%
1 29
1.8%
1.1 52
3.3%
1.15 1
 
0.1%
1.2 66
4.2%
1.3 50
3.2%
1.4 51
3.2%
1.45 1
 
0.1%
ValueCountFrequency (%)
22.6 1
 
0.1%
20.3 1
 
0.1%
19.95 1
 
0.1%
19.4 1
 
0.1%
19.3 3
0.2%
19.25 2
0.1%
18.75 1
 
0.1%
18.5 1
 
0.1%
18.4 1
 
0.1%
18.35 1
 
0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045269061
Minimum0.009
Maximum0.271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:28.776155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.035
median0.043
Q30.05
95-th percentile0.064
Maximum0.271
Range0.262
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.020793437
Coefficient of variation (CV)0.45932999
Kurtosis30.079414
Mean0.045269061
Median Absolute Deviation (MAD)0.007
Skewness4.5734841
Sum71.842
Variance0.00043236703
MonotonicityNot monotonic
2024-11-06T19:17:28.927695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 76
 
4.8%
0.044 69
 
4.3%
0.048 68
 
4.3%
0.042 58
 
3.7%
0.047 58
 
3.7%
0.05 57
 
3.6%
0.04 54
 
3.4%
0.035 54
 
3.4%
0.041 52
 
3.3%
0.037 51
 
3.2%
Other values (88) 990
62.4%
ValueCountFrequency (%)
0.009 1
 
0.1%
0.013 1
 
0.1%
0.014 2
 
0.1%
0.015 4
0.3%
0.016 1
 
0.1%
0.017 3
0.2%
0.018 4
0.3%
0.019 1
 
0.1%
0.02 6
0.4%
0.021 5
0.3%
ValueCountFrequency (%)
0.271 1
0.1%
0.212 1
0.1%
0.209 1
0.1%
0.208 1
0.1%
0.194 1
0.1%
0.185 1
0.1%
0.184 2
0.1%
0.176 2
0.1%
0.175 2
0.1%
0.174 2
0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.940769
Minimum2
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:29.052308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q123
median33
Q345
95-th percentile63
Maximum124
Range122
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.409145
Coefficient of variation (CV)0.46962748
Kurtosis1.9180242
Mean34.940769
Median Absolute Deviation (MAD)11
Skewness0.93245134
Sum55451
Variance269.26005
MonotonicityNot monotonic
2024-11-06T19:17:29.187550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 56
 
3.5%
26 49
 
3.1%
31 47
 
3.0%
25 45
 
2.8%
36 44
 
2.8%
45 42
 
2.6%
24 41
 
2.6%
34 41
 
2.6%
33 40
 
2.5%
20 40
 
2.5%
Other values (84) 1142
72.0%
ValueCountFrequency (%)
2 1
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 6
 
0.4%
6 12
0.8%
7 8
0.5%
8 7
0.4%
9 5
 
0.3%
10 15
0.9%
11 12
0.8%
ValueCountFrequency (%)
124 1
 
0.1%
112 1
 
0.1%
108 3
0.2%
105 2
0.1%
101 2
0.1%
98 3
0.2%
97 1
 
0.1%
87 2
0.1%
81 3
0.2%
79.5 4
0.3%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct181
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.86232
Minimum9
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:29.371679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile75
Q1102.5
median124
Q3153.5
95-th percentile193
Maximum259
Range250
Interquartile range (IQR)51

Descriptive statistics

Standard deviation37.122901
Coefficient of variation (CV)0.2880819
Kurtosis-0.075776936
Mean128.86232
Median Absolute Deviation (MAD)25
Skewness0.34774478
Sum204504.5
Variance1378.1098
MonotonicityNot monotonic
2024-11-06T19:17:29.502159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 36
 
2.3%
113 34
 
2.1%
122 29
 
1.8%
120 25
 
1.6%
134 24
 
1.5%
149 24
 
1.5%
140 24
 
1.5%
118 23
 
1.4%
116 22
 
1.4%
95 22
 
1.4%
Other values (171) 1324
83.4%
ValueCountFrequency (%)
9 1
 
0.1%
10 1
 
0.1%
31 1
 
0.1%
34 1
 
0.1%
40 3
0.2%
41 2
0.1%
44 1
 
0.1%
47 2
0.1%
49 3
0.2%
50 3
0.2%
ValueCountFrequency (%)
259 1
 
0.1%
251 1
 
0.1%
248 2
0.1%
243 1
 
0.1%
240 1
 
0.1%
230 1
 
0.1%
227 1
 
0.1%
226 1
 
0.1%
224 2
0.1%
223 3
0.2%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct648
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99322592
Minimum0.98711
Maximum0.99971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:29.662562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.989206
Q10.99091
median0.99275
Q30.9954
95-th percentile0.9984
Maximum0.99971
Range0.0126
Interquartile range (IQR)0.00449

Descriptive statistics

Standard deviation0.0028974243
Coefficient of variation (CV)0.0029171855
Kurtosis-0.82889023
Mean0.99322592
Median Absolute Deviation (MAD)0.00217
Skewness0.36010481
Sum1576.2495
Variance8.3950675 × 10-6
MonotonicityNot monotonic
2024-11-06T19:17:29.827790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9984 15
 
0.9%
0.99138 9
 
0.6%
0.99226 9
 
0.6%
0.99792 9
 
0.6%
0.99058 9
 
0.6%
0.99176 8
 
0.5%
0.99807 8
 
0.5%
0.99666 8
 
0.5%
0.99184 8
 
0.5%
0.99362 7
 
0.4%
Other values (638) 1497
94.3%
ValueCountFrequency (%)
0.98711 1
0.1%
0.98722 1
0.1%
0.9874 1
0.1%
0.98742 2
0.1%
0.98746 2
0.1%
0.98758 1
0.1%
0.98774 1
0.1%
0.98779 1
0.1%
0.98794 2
0.1%
0.98816 1
0.1%
ValueCountFrequency (%)
0.99971 2
0.1%
0.99966 1
 
0.1%
0.99956 2
0.1%
0.99954 2
0.1%
0.99947 2
0.1%
0.99946 2
0.1%
0.99945 3
0.2%
0.99943 1
 
0.1%
0.99942 1
 
0.1%
0.99936 1
 
0.1%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1620983
Minimum2.79
Maximum3.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:29.995800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.79
5-th percentile2.933
Q13.07
median3.16
Q33.25
95-th percentile3.39
Maximum3.76
Range0.97
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.13980646
Coefficient of variation (CV)0.044213193
Kurtosis0.63352619
Mean3.1620983
Median Absolute Deviation (MAD)0.09
Skewness0.3387998
Sum5018.25
Variance0.019545847
MonotonicityNot monotonic
2024-11-06T19:17:30.149643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 64
 
4.0%
3.14 57
 
3.6%
3.24 54
 
3.4%
3.18 52
 
3.3%
3.22 51
 
3.2%
3.1 51
 
3.2%
3.2 49
 
3.1%
3.12 48
 
3.0%
3.08 48
 
3.0%
3.11 45
 
2.8%
Other values (73) 1068
67.3%
ValueCountFrequency (%)
2.79 1
 
0.1%
2.8 1
 
0.1%
2.82 1
 
0.1%
2.83 4
 
0.3%
2.85 3
 
0.2%
2.86 7
 
0.4%
2.87 3
 
0.2%
2.88 9
 
0.6%
2.89 1
 
0.1%
2.9 23
1.4%
ValueCountFrequency (%)
3.76 1
 
0.1%
3.75 2
0.1%
3.67 1
 
0.1%
3.66 3
0.2%
3.63 1
 
0.1%
3.59 1
 
0.1%
3.57 1
 
0.1%
3.56 2
0.1%
3.55 2
0.1%
3.54 1
 
0.1%

sulphates
Real number (ℝ)

Distinct67
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49006931
Minimum0.23
Maximum1.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:30.286422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.34
Q10.41
median0.48
Q30.55
95-th percentile0.7
Maximum1.08
Range0.85
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11293598
Coefficient of variation (CV)0.23044899
Kurtosis2.1369072
Mean0.49006931
Median Absolute Deviation (MAD)0.07
Skewness1.0178153
Sum777.74
Variance0.012754535
MonotonicityNot monotonic
2024-11-06T19:17:30.555629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 93
 
5.9%
0.46 65
 
4.1%
0.44 63
 
4.0%
0.48 63
 
4.0%
0.38 62
 
3.9%
0.45 61
 
3.8%
0.54 58
 
3.7%
0.56 55
 
3.5%
0.52 55
 
3.5%
0.49 55
 
3.5%
Other values (57) 957
60.3%
ValueCountFrequency (%)
0.23 1
 
0.1%
0.25 1
 
0.1%
0.26 3
 
0.2%
0.27 6
 
0.4%
0.28 2
 
0.1%
0.29 5
 
0.3%
0.3 9
0.6%
0.31 15
0.9%
0.32 11
0.7%
0.33 16
1.0%
ValueCountFrequency (%)
1.08 1
 
0.1%
1.01 1
 
0.1%
0.98 5
0.3%
0.96 2
 
0.1%
0.94 1
 
0.1%
0.92 1
 
0.1%
0.88 2
 
0.1%
0.85 1
 
0.1%
0.83 2
 
0.1%
0.82 1
 
0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.849439
Minimum8.4
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:30.802901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9
Q19.7
median10.8
Q311.9
95-th percentile13
Maximum14.2
Range5.8
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.3084367
Coefficient of variation (CV)0.12059948
Kurtosis-0.95295073
Mean10.849439
Median Absolute Deviation (MAD)1.1
Skewness0.22462414
Sum17218.06
Variance1.7120067
MonotonicityNot monotonic
2024-11-06T19:17:30.942015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 67
 
4.2%
11 61
 
3.8%
9.5 61
 
3.8%
9.2 57
 
3.6%
11.2 54
 
3.4%
9.1 48
 
3.0%
10.4 46
 
2.9%
10.8 44
 
2.8%
11.1 43
 
2.7%
11.3 42
 
2.6%
Other values (70) 1064
67.0%
ValueCountFrequency (%)
8.4 3
 
0.2%
8.5 1
 
0.1%
8.6 2
 
0.1%
8.7 15
 
0.9%
8.8 31
2.0%
8.9 16
 
1.0%
9 37
2.3%
9.1 48
3.0%
9.2 57
3.6%
9.3 26
1.6%
ValueCountFrequency (%)
14.2 1
 
0.1%
14.05 1
 
0.1%
14 2
 
0.1%
13.9 2
 
0.1%
13.8 2
 
0.1%
13.7 3
 
0.2%
13.6 9
0.6%
13.55 1
 
0.1%
13.5 5
 
0.3%
13.4 15
0.9%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9344675
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2024-11-06T19:17:31.059787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82489046
Coefficient of variation (CV)0.13899991
Kurtosis0.23271995
Mean5.9344675
Median Absolute Deviation (MAD)1
Skewness0.11541215
Sum9418
Variance0.68044428
MonotonicityNot monotonic
2024-11-06T19:17:31.159890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 792
49.9%
5 410
25.8%
7 293
 
18.5%
8 50
 
3.2%
4 39
 
2.5%
3 3
 
0.2%
ValueCountFrequency (%)
3 3
 
0.2%
4 39
 
2.5%
5 410
25.8%
6 792
49.9%
7 293
 
18.5%
8 50
 
3.2%
ValueCountFrequency (%)
8 50
 
3.2%
7 293
 
18.5%
6 792
49.9%
5 410
25.8%
4 39
 
2.5%
3 3
 
0.2%

Interactions

2024-11-06T19:17:25.841579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:11.532131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.811135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.970782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.119754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.370719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.692444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.943346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.143108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.492196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.703198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.598757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.936092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:11.622240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.907152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.070296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.213134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.466469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.795973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.037692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.240492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.588484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.850369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.701582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.025762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:11.716614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.000523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.159019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.305499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.559948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.906620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.136376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.342075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.679984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:23.029279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.804772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.119675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:11.816395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.100263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.255859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.534700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.671614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.011063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.237461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.440990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.781066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:23.215586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.909083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.238931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:11.908301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.188838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.348623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.620697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.777918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.110130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.326532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.536653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.902657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:23.395577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.008714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.334748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.001585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.289567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.444404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.716958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.892926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.216470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.437664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.641926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.995720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:23.548230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.107433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.436128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.099961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.390615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.543854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.810389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.045176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.326090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.547296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.748673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.105125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:23.705685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.213314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.535096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.191912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.491181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.640406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.906889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.156635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.430867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.642384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.856267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.211193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:23.835757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.318775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.636271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.291198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.592623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.743947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.004014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.277973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.537503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.751258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.958716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.318398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.015236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.439577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.729448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.437882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.685570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.838852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.094691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.389347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.638714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.855007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.191425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.410733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.178621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.545891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.823956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.605099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.775775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:14.933251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.185769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.491467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.738695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:19.947094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.293233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.503830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.319990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.645150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:26.926090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:12.710390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:13.874326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:15.026748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:16.276772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:17.590935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:18.840818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:20.044390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:21.394145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:22.608927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:24.476190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T19:17:25.746921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-06T19:17:31.251223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.6020.012-0.849-0.159-0.2470.2420.447-0.469-0.067-0.4470.141
chlorides-0.6021.0000.0210.5160.1370.148-0.119-0.3430.2330.0800.368-0.032
citric acid0.0120.0211.000-0.0030.2080.079-0.0470.024-0.0660.1770.071-0.160
density-0.8490.516-0.0031.0000.2520.279-0.202-0.3550.8020.1070.513-0.061
fixed acidity-0.1590.1370.2080.2521.000-0.005-0.538-0.1000.1430.0350.091-0.036
free sulfur dioxide-0.2470.1480.0790.279-0.0051.000-0.0150.0000.2550.0750.581-0.074
pH0.242-0.119-0.047-0.202-0.538-0.0151.0000.058-0.2110.0840.0040.086
quality0.447-0.3430.024-0.355-0.1000.0000.0581.000-0.060-0.021-0.217-0.204
residual sugar-0.4690.233-0.0660.8020.1430.255-0.211-0.0601.0000.0220.3840.044
sulphates-0.0670.0800.1770.1070.0350.0750.084-0.0210.0221.0000.150-0.006
total sulfur dioxide-0.4470.3680.0710.5130.0910.5810.004-0.2170.3840.1501.0000.108
volatile acidity0.141-0.032-0.160-0.061-0.036-0.0740.086-0.2040.044-0.0060.1081.000

Missing values

2024-11-06T19:17:27.060874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T19:17:27.251598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
08.10.240.3210.50.03034.0105.00.994073.110.4211.86
15.80.230.202.00.04339.0154.00.992263.210.3910.26
27.50.330.362.60.05126.0126.00.990973.320.5312.76
36.60.380.369.20.06142.0214.00.997603.310.569.45
46.40.150.291.80.04421.0115.00.991663.100.3810.25
56.50.320.345.70.04427.091.00.991843.280.6012.07
67.50.220.322.40.04529.0100.00.991353.080.6011.37
76.40.230.321.90.03840.0118.00.990743.320.5311.87
86.10.220.311.40.03940.0129.00.991933.450.5910.95
96.50.480.020.90.04332.099.00.992263.140.479.84
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
16226.80.2200.361.200.05238.0127.00.993303.040.549.25
16234.90.2350.2711.750.03034.0118.00.995403.070.509.46
16246.10.3400.292.200.03625.0100.00.989383.060.4411.86
16255.70.2100.320.900.03838.0121.00.990743.240.4610.66
16266.50.2300.381.300.03229.0112.00.992983.290.549.75
16276.20.2100.291.600.03924.092.00.991143.270.5011.26
16286.60.3200.368.000.04757.0168.00.994903.150.469.65
16296.50.2400.191.200.04130.0111.00.992542.990.469.46
16305.50.2900.301.100.02220.0110.00.988693.340.3812.87
16316.00.2100.380.800.02022.098.00.989413.260.3211.86

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
1917.00.150.2814.700.05129.0149.00.997922.960.399.078
2257.30.190.2713.900.05745.0155.00.998072.940.418.888
2317.40.160.3013.700.05633.0168.00.998252.900.448.777
2307.40.160.2715.500.05025.0135.00.998402.900.438.776
125.70.220.2016.000.04441.0113.00.998623.220.468.965
1236.60.220.2317.300.04737.0118.00.999063.080.468.865
1406.70.160.3212.500.03518.0156.00.996662.880.369.065
2377.50.240.3113.100.05026.0180.00.998843.050.539.165
135.70.220.2216.650.04439.0110.00.998553.240.489.064
276.00.200.266.800.04922.093.00.992803.150.4211.064